regions

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the behavioral disclosure burden. It does disclose the algorithmic approach (HDBSCAN) and that the tool 'Returns cluster info', suggesting a read/analysis behavior, but it does not mention side effects, scope, rate limits, or edge cases.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short, information-dense sentences with the core action and algorithm front-loaded. There is no fluff or redundant repetition of the tool name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the return-value summary ('cluster info with representative memories and keywords') carries the output burden and is adequate but leaves the exact cluster fields unspecified. With no required parameters and complete schema coverage of the optional parameters, the definition is nearly complete for invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and all five optional parameters are already documented in the input schema. The description adds no extra parameter-level meaning, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific action ('Discover semantic clusters') plus the algorithm ('using HDBSCAN') and resource ('memory space'). The return value ('cluster info with representative memories and keywords') further differentiates it from memory operations like recall or nearby.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use it: when an agent needs semantic clusters of memories. However, it gives no explicit guidance on when to choose this tool over siblings such as discover_connections or nearby, and no exclusions or prerequisites are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.